SCC-IRG Track 1: Community Based Approach to Address Contaminants in Drinking Water using Smart Cloud-Connected Electrochemical Sensors

清洁和安全的水是一个社区生存和发展的基本必需品。然而，数以百万计的人暴露于不安全水平的饮用水污染物，包括有毒和持久性重金属以及无处不在的“永久化学品”，如全氟烷基物质和多氟烷基物质。尽管有严格的法规和完善的实验室方法来检测这些广泛和持久的污染物，但由于采样和测试不频繁，这些污染物有时未被检测到。在这个项目中，麻省大学洛厄尔分校的工程师、计算机科学家和社会科学家将与社区利益相关者（居民、社区团体、非营利组织、饮用水公用事业公司和监管机构）密切合作，在马萨诸塞州的几个社会经济多样化的社区试点一个智能物联网（IoT）支持的水质监测和警报系统。鉴于饮用水污染和暴露在经济和种族上处于劣势的社区中发生的比例过高，这些社区的基础设施较旧，拟议中的技术将使弱势群体能够利用这些数据来倡导补救措施。采用跨学科的社会技术系统方法来实施智能社区参与的水质监测和警报系统，将成为解决类似大规模社会和基础设施问题的新范例。在这个SCC项目中，研究人员将(1)部署由公民科学家操作的电化学电子舌（E-Tongue）设备，对铅和砷等污染物进行快速、现场的水质检测；(2)与社区利益相关者共同设计一个用户友好的应用程序和云计算平台，用于数据分析；(3)促进社区利益相关者之间的共享学习和协作，以建立社会凝聚力和对水检测技术和地方当局的信任。此外，这项工作将开发时空机器学习算法和云计算平台，该平台将从单个E-Tongue设备获取响应，并产生污染物类型、浓度、可能来源和污染程度的预测。这些信息将用于迅速通知公共卫生当局进行干预，并提醒受影响的居民采取适当行动。通过智能传感和云计算系统的设计、开发和测试，拟议的变革性研究将有助于对实时水污染物威胁检测和预警系统的新型时空分析、移动计算和机器学习技术的基本理解和实际设计。这项研究还将提高我们对技术、培训和关系的认识和理解，以促进可持续的、可扩展的水质检测传感器平台，并提高对水检测技术和地方当局的认识和社会信任。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Clean and safe water is a basic necessity for a community to survive and thrive. However, millions of people are exposed to unsafe levels of drinking water contaminants including toxic and persistent heavy metals and ubiquitous “forever chemicals” such as per– and polyfluroalkyl substances (PFAS). Despite strict regulations, and well-established laboratory methods for detecting these widespread and persistent contaminants, these pollutants sometimes go undetected because of infrequent sampling and testing. In this project engineers, computer scientists, and social scientists from the University of Massachusetts Lowell will work closely with community stakeholders (residents, neighborhood groups, nonprofits, drinking water utilities, and regulators) to pilot a smart Internet of Things (IoT) enabled water-quality monitoring and alert system in several socio-economically diverse communities of Massachusetts. Given that drinking water contamination and exposure occurs disproportionately in economically and racially disadvantaged communities with older infrastructure, the proposed technology will empower underprivileged groups to use the data to advocate for remediation efforts. The transdisciplinary sociotechnical systems approach to implement a smart community engaged water-quality monitoring and alert system will be a new paradigm for addressing similar large scale societal and infrastructural problems.In this SCC project, the investigators will (1) deploy citizen-scientist-operated electrochemical electronic tongue (E-Tongue) devices for rapid, onsite, water quality testing of contaminants such as lead and arsenic, (2) co-design with community stakeholders a user-friendly app and cloud-computing platform for data analysis, and (3) foster shared learning and collaboration among community stakeholders to build social cohesion and trust in water testing technologies and the local authorities. Furthermore, this work will develop spatiotemporal machine learning algorithms and a cloud-computing platform that will take the responses from the individual E-Tongue devices and produce predictions of contaminant type, concentration, probable source, and extent of the contamination. This information will be used to quickly notify the public health authorities for intervention and alert affected residents to take appropriate actions. Through the design, development, and testing of a smart sensing and cloud-computing system, the proposed transformative research will contribute to the fundamental understanding and practical design of novel spatiotemporal analytics, mobile computing, and machine learning techniques for real-time water contaminant threat detection and early warning systems. The research will also advance our knowledge and understanding of the technologies, training, and relationships required to facilitate a sustainable, scalable sensor platform for water quality testing and increase awareness and social trust in water testing technologies and local authorities.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.